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The global AI agenda examines the potential for companies across industries to share data in new ways that result in value for businesses and consumers.

EmTech Asia conference is all set to beheld on 25-26 February 2020 at the Marina Bay Sands Expo and Convention Centre in Singapore.

EmTech Asia 2020, a conference co-organised by MIT Technology Review and Koelnmesse, tackles the granular issues that the tech community is facing everywhere. Key researchers and industry figureheads will discuss AI, its potential impact on society, and address questions of ethics and AI.

EmTech Asia speaker Toby Walsh, Scientia Professor of Artificial Intelligence at Technical University Berlin and University of New South Wales says, Weve seen many examples of technology companies behaving in ways that challenge us. But what is it about AI that is different than other technologies which have touched our lives in the past? What old ethical issues does AI put on steroids? And what new ethical issues does AI bring to the table for the first time?

At the conference, Professor Walsh will examine a range of technological challenges from autonomous cars through predictive analytics to killer robots that may present ethical challenges.

Issues of ethics around AI calls into question the design of intelligent systems, as machines increasingly make decisions on behalf of humans. Roland Chin, President and Vice-chancellor of Hong Kong Baptist University believes that the design of intelligent systems and relevant decision-making processes needs to align with accepted moral values and ethical principles.

The challenge is not just to code fixed ethical values into intelligent systems but also to operationalise diverse and evolving ethical values across cultures and nations. Bottom-up approaches seek the shared ethical values of the crowd by involving many people to arrive at accepted decisions about ethical dilemmas, whereas top-down approaches rely on philosophers to develop principles from humankinds collective ethical wisdom amassed over generations and across cultures, he says.

During his presentation, Professor Chin will call for a holistic approach; integrative learning in ethical awareness and competences, instead of treating ethics as an add-on subject in STEM programs.

While AI can be used for good, it also has a dark side.

Co-author ofGhost Work, How to Stop Silicon Valley from Building a Global Underclass, Mary L. Gray, Principal Researcher at Microsoft Research, questions the good of AI. At EmTech Asia, she will discuss how, although AI has been heralded as a salve for much of the webs ills and a revolutionary tool for humankind, its not a panacea, and that in reality, there are armies of human labourers being used by tech companies to step in when AI doesnt work. She poses the question, What will the future of this workforce be, and how can labour laws adapt to meet changing needs?.

It is important that the development of AI creates new opportunities to improve the lives of people around the world. Speaker Peter Norvig, Director of Research, Google Inc. says that the subject is also raising new questions about the best way to build fairness into these systems. At EmTech Asia, Peter will explore these issues and outline what AI scientists can do to build fairer AI systems.

Overview of AI

Claire Beatty, Editorial Director, MIT Technology Review Insights will present the latest trends in AI and the outlook for Corporate Data Alliances at EmTech Asia.

Along with insights into trends in global AI adoption, focusing on the leading use cases, challenges, and risks involved, Claire will also present the results of a survey of 1,000 business leaders worldwide, "The global AI agenda examines the potential for companies across industries to share data in new ways that result in value for businesses and consumers.

Erin Bradner, Director, Robotics Lab, Autodesk will outline technology trends expected to shape how industrial robots will look and operate in the future. These trends include machine learning, modular robotics, closed-loop control, new user interfaces, and advanced computer simulation. She draws on research from academia and industry to help us understand why robots will become increasingly more adaptable, flexible, and interconnected.

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EmTech Asia 2020 Explores the Ethics of Artificial Intelligence - BSA bureau

Spending on Artificial Intelligence (AI) is expected to more than double from $35 billion in 2019 to $79 billion in 2022, according to IDC forecasts. But as we enter the fourth industrial revolution powered by AI, technologists have divided themselves into utopian and alarmists camps. Thats a false and dangerous dichotomy. We need to adopt a pragmatic mindset that sees AI as both a risk and a way to manage risk.

From killer robots to racism, todays headlines provide AI alarmists with ample fodder. The risks associated with AI grow as technology improves and proliferates. But unlike other paradigm-shifting technologies like the printing press, mass production, or digital commerce, its the invisible aspects of AI that we most need to worry about: algorithms that learn from patterns and can trigger costly errors and, left unchecked, can pull projects and organizations in entirely wrong directions with catastrophic consequences.

For the first time in history, a single person can customize a message for billions and share it with them within a matter of days. A software engineer can create an army of AI-powered bots, each pretending to be a different person, promoting biased content on behalf of political or commercial interests or worse, attack vulnerable systems.

Read More: How CMOs Succeed with AI-Powered CX

The doomsday scenarios arent a fait accompli, but they do underscore the need for AI systems that engage with humans in transparent ways. Every time a new technology is introduced, it creates new challenges, safety issues, and potential hazards. For example, when pharmaceuticals were first introduced, there were no safety tests, quality standards, childproof caps or tamper-resistant packages. AI is a new technology and will undergo a similar evolution.

To trust an AI system, we must have confidence in its decisions. Increasingly, bankers are asking important questions about how AI will affect consumers. The Defense Department has signaled that it understands the importance of empowering ethicists to guide AI technologies.

Meanwhile, were beginning to include AI in our long-overdue conversations about criminal justice. These are all good signs, but we need to rapidly scale our ethical inquiries by using supervisory AI systems to provide visibility and control over production AI systems.

Read More: Shaped by AI, the Future of Work Sees Soft Skills and Creativity as Essential

AI systems must reflect our values. We can do this through investment, education, and policy. But first, we must dispense with the utopian and alarmist positions. Utopians assume that every AI solution will automatically be an improvement over what came before it, and therefore miss the opportunity to address critical questions about values before deployment.

At the opposite end of the spectrum, alarmists assume the worst and therefore fail to show up to the debate. A pragmatic approach that sees AI as both a risk and a way to manage risk by pairing AI with other AI is the prerequisite mental model for grappling with the issues raised by the fourth industrial revolution.

Read More: Efficient Ways the AI Will Boost Your E-Commerce Sales

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Why Artificial Intelligence is Both a Risk and a Way to Manage Risk - AiThority

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Global Artificial Intelligence in Fintech Market Size, Analytical Overview, Growth Factors, Demand, Trends and Forecast to 2026 - Technology Magazine

A new survey released by the World Economic Forum and the Cambridge Centre for Alternative Finance (CCAF) finds nearly two-thirds (64%) of financial services leaders expect to be mass adopters of Artificial Intelligence in two years compared to just 16% doing so today. These firms plan to expand AI use to purposes beyond cost reduction, using AI for revenue generation, process automation, risk management, customer service and client acquisition.

InTransforming Paradigms: Global AI in Financial Services Survey,over 150 senior financial services executives in both fintech and incumbent financial institutions responded to a range of questions on the impact AI will have on the industry, concluding that there will be a significant gap between firms that quickly implement AI and firms that lag behind.

Currently, 60% of firms invest less than 10% of their R&D resources on AI despite evidence of accelerating returns. Pay offs have shown to be especially strong between investment levels of 10% and 30% as well as investment levels of 30% and >40%.

The comprehensive and global study confirms that AI is affecting the financial system at an accelerating pace, says Matthew Blake, Head of Financial and Monetary Systems at the World Economic Forum. With the rising trend of mass adoption of the technologies throughout financial services, those firms that implement AI quickly look set to sprint ahead.

The study has also revealed executive fears surrounding AI bias and market-wide risks, with over half of executives saying they expect mass AI adoption to worsen bias and discrimination within the sector. Other market-wide risks were also identified.

This is a worry, but 70% of respondents also believe they are at least somewhat prepared to mitigate AI bias risks. Generally, firms using Risk and Compliance teams in AI implementation are most confident about their chances.

The report also identified a difference between how fintechs and incumbent firms are expecting to use AI in their businesses. For example, a higher share of fintechs are creating AI-based products and services, employ autonomous decision-making systems, and rely on cloud-based offerings. Meanwhile, traditional financial services players predominantly focus on harnessing AI to improve existing products.

This empirical research underscores the growing importance of harnessing AI in financial services, says Bryan Zhang, Executive Director of the Cambridge Centre for Alternative Finance, which gives new impetus for firms to develop a holistic and future-proof AI strategy.

TheGlobal AI in Financial Services Survey,which was produced in collaboration with EY and Invesco, looks into many areas of AI adoption in financial services. The reports other major findings include:

AI is transforming the financial services industry and we can expect widespread adoption to continue, says Nigel Duffy, EY Global Artificial Intelligence Leader. As the technologies start to disrupt business models and transform business functions, its increasingly important for organizations to focus on the long-term implications of AI adoption: trust in AI, workforce transformation, and how customer and stakeholder value can be radically reimagined.

The report highlights the amazing opportunity ahead of us in financial services for using artificial intelligence and machine learning to the benefits of our customers and our organizations, says Donie Lochan, Chief Technology Officer, Invesco. Technological advances such as leveraging intelligence to define investments for customers tied to their personalized goals, improving customer experience through the use of intelligent bots, additional alpha generation via insights from alternative datasets, and operational efficiencies through machine learning automation, will soon become the norm for our industry.

Overall, this survey highlights the profound shift AI is bringing to the financial services industry. As companies begin to leverage AI to increase profitability and achieve scale, more changes can be expected within the industry and for consumers.

Please Donate Today Did you enjoy this article? Then please consider donating today to ensure that Eurasia Review can continue to be able to provide similar content.

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Artificial Intelligence Will Transform Financial Services Industry Within Two Years, Survey Finds - Eurasia Review

Lately, we've seen many "x-Ops" management practices appear on the scene, all derivatives from DevOps, which seeks to coordinate the output of developers and operations teams into a smooth, consistent and rapid flow of software releases. Another emerging practice, DataOps, seeks to achieve a similarly smooth, consistent and rapid flow of data through enterprises. Like many things these days, DataOps is spilling over from the large Internet companies, who process petabytes and exabytes of information on a daily basis.

Such an uninhibited data flow is increasingly vital to enterprises seeking to become more data-driven and scale artificial intelligence and machine learning to the point where these technologies can have strategic impact.

Awareness of DataOps is high. A recent survey of 300 companies by 451 Research finds 72 percent have active DataOps efforts underway, and the remaining 28 percent are planning to do so over the coming year. A majority, 86 percent, are increasing their spend on DataOps projects to over the next 12 months. Most of this spending will go to analytics, self-service data access, data virtualization, and data preparation efforts.

In the report, 451 Research analyst Matt Aslett defines DataOps as "The alignment of people, processes and technology to enable more agile and automated approaches to data management."

The catch is "most enterprises are unprepared, often because of behavioral norms -- like territorial data hoarding -- and because they lag in their technical capabilities -- often stuck with cumbersome extract, transform, and load (ETL) and master data management (MDM) systems," according to Andy Palmer and a team of co-authors in their latest report,Getting DataOps Right, published by O'Reilly. Across most enterprises, data is siloed, disconnected, and generally inaccessible. There is also an abundance of data that is completely undiscovered, of which decision-makers are not even aware.

Here are some of Palmer's recommendations for building and shaping a well-functioning DataOps ecosystem:

Keep it open: The ecosystem in DataOps should resemble DevOps ecosystems in which there are many best-of-breed free and open source software and proprietary tools that are expected to interoperate via APIs." This also includes carefully evaluating and selecting from the raft of tools that have been developed by the large internet companies.

Automate it all:The collection, ingestion, organizing, storage and surfacing of massive amounts of data at as close to a near-real-time pace as possible has become almost impossible for humans to manage. Let the machines do it, Palmer urges. Areas ripe for automaton include "operations, repeatability, automated testing, and release of data." Look to the ways DevOps is facilitating the automation of the software build, test, and release process, he points out.

Process data in both batch and streaming modes. While DataOps is about real-time delivery of data, there's still a place -- and reason -- for batch mode as well. "The success of Kafka and similar design patterns has validated that a healthy next-generation data ecosystem includes the ability to simultaneously process data from source to consumption in both batch and streaming modes," Palmer points out.

Track data lineage: Trust in the data is the single most important element in a data-driven enterprise, and it simply may cease to function without it. That's why well-thought-out data governance and a metadata (data about data) layer is important. "A focus on data lineage and processing tracking across the data ecosystem results in reproducibility going up and confidence in data increasing," says Palmer.

Have layered interfaces. Everyone touches data in different ways. "Some power users need to access data in its raw form, whereas others just want to get responses to inquiries that are well formulated," Palmer says. That's why a layered set of services and design patterns is required for the different personas of users. Palmer says there are three approaches to meeting these multilayered requirements:

Business leaders are increasingly leaning on their technology leaders and teams to transform their organizations into data-driven digital entities that can react to events and opportunities almost instantaneously. The best way to accomplish this -- especially with the meager budgets and limited support that gets thrown out with this mandate -- is to align the way data flows from source to storage.

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Artificial intelligence requires trusted data, and a healthy DataOps ecosystem - ZDNet